How Luminate Processes 3.5TB of Entertainment Data 334% Faster with Snowflake
Luminate powers the Billboard music charts and provides data intelligence across music, film, and television for major record labels, studios, and talent agencies. After migrating from on-premises Spark and SQL Server to Snowflake, the company achieved 334% faster daily data processing across more than 3.5 terabytes of daily input. Market reports that previously took a full month now run overnight, and Luminate can for the first time deliver cross-industry insights correlating music and TV consumption.
Impact
334%
Increase in daily data processing speed
3.5TB+
Daily data volume processed
Overnight
Market report turnaround time
Challenge
Legacy on-premises infrastructure could not process 3.5TB of daily entertainment data fast enough, forcing market reports to take a full month to complete and preventing cross-industry analysis between music and TV consumption datasets.
Solution
Luminate migrated to Snowflake as the core data lake, using Snowflake Secure Data Sharing for near-real-time client delivery, Snowpark ML for Python-based modeling, and independent storage/compute scaling to accelerate all data processing pipelines.
Tools & Technologies
What Leaders Say
“We have years of data that we were never able to glean deeper insights from before Snowflake. Essentially, we were just building rank lists.”
“We’re coming up on three years of working with Snowflake, and it’s still the obvious choice. We feel completely happy with the decision we made.”
“The ability to apply all the Python code and models directly on top of data is especially beneficial.”
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Full Story
Every song streamed, every episode watched — Luminate captures it and turns raw playback data into the authoritative charts and market intelligence that drive royalties, awards, and industry strategy. With hundreds of verified sources feeding more than 3.5 terabytes of data daily, the company’s ability to deliver timely, accurate insights is not just a product differentiator; it defines its credibility with record labels, studios, and streaming platforms worldwide.
Before Snowflake, Luminate’s infrastructure was built on legacy on-premises tools that Chief Data Officer Glenn Walker described as “woefully outdated.” The platform could not scale to match the complexity of multi-source entertainment data, and processing delays meant that market reports — outputs that clients needed to make time-sensitive decisions — could take a full month to produce. More fundamentally, the siloed architecture meant Luminate could analyze data within categories but never across them.
The migration to Snowflake consolidated all data into a single cloud data lake with independently scalable storage and compute. Snowflake Secure Data Sharing replaced days-long data delivery processes with near-real-time access for clients and partners. Snowpark and Snowpark ML allowed Luminate’s data science team to run Python models directly on top of data without moving it, dramatically streamlining the experimentation cycle for AI/ML product development.
The impact was structural. Daily record processing improved 334%, converting overnight what previously required weeks. This enabled Luminate to build its Streaming Viewership Model (SVM), a statistical model that estimates streaming consumption from structured and unstructured sources — a problem no competitor had solved. More critically, having all data on one platform for the first time unlocked true cross-industry analysis: Luminate can now measure the direct effect of a TV show featuring an artist on that artist’s streaming numbers, a capability that changes the value proposition for every client.
Luminate is now three years into its Snowflake deployment, actively exploring generative AI integrations via Snowflake Cortex AI, and expanding its private Marketplace listings for seamless client data delivery.